Geospatial Crowdsourced Data - Validation and Classification

Dear Colleagues,

In the last decade, there was exponential growth in the number of projects that collect Crowdsourced Geospatial Data (CGD), also called Volunteered Geographical Information (VGI). This allows the collection of large volumes of data of many different types, such as georeferenced photographs (e.g., using Flickr or Instagram), tweets, the classification of what occurs at predefined locations (e.g., campaigns of GeoWiki or Ushahidi), the creation and classification of digital objects (e.g., vector data creation and classification in OpenStreetMap or Wikimapia) or the reporting of events (e.g., at portals of municipalities or Facebook).

This new way of collecting data, provided by regular citizens, potentially allows the obtainment of data that would not be otherwise available. However, this also means that there is usually no reference data available to assess their accuracy, which is one of the most important topics concerning the use of CGD for operational or scientific applications. Moreover, one of the main problems related to crowdsourced data is the quality of the data collected, as volunteers may create data with errors and/or limitations, such as the assignment of incorrect classifications to identified objects in an aerial or satellite images, geolocation with errors or low precision, uploading of wrong data, differences of interpretation among volunteers, incorrect use of the available tools to collect data, etc. All this makes CGD validation a hot topic, and further developments in the near future to make full use of the potential of CGD are needed.

On the other hand, the collection of large quantities of data requires the use of data mining and aggregation techniques to extract useful information to support decision making, thus increasing the value of using this type of data.

In the above context, we would like to invite you to submit original articles presenting results or new methods, frameworks or methodologies for CGD validation and classification, which may depend only on collected CGD or may include other types of data, such as data collected by physical sensors or remote sensing techniques. Articles about the communication of CGD quality, uncertainty or the extracted information are also welcome. Potential topics include, but are not limited to:

• Big data
• Classification
• Data cleaning
• Data integration
• Data mining
• Data validation
• Data analytics
• Performance of Methods
• Physical sensors
• Quality assessment
• Remote sensors
• Uncertainty
• Visualization

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